Meeting the Challenges of RADV Expansion
Governmental Pressure Makes Efficiency and Transparency More Important than Ever

According to documents released earlier this year, the Government Accountability Office (GAO) discovered that the Centers for Medicare and Medicaid Services (CMS) had failed to recover up to $125 million in Medicare overpayments in 2007, alone.1 The GAO criticized CMS for failing to address data accuracy, an issue of increasing concern as CMS intends to rely on complex Encounter Data System submissions as the basis for member risk scores exclusively by 2020.

Health plans are likely to push back on that goal, asserting that the 2020 deadline does not allow time for a truly efficient, and thus successful, transition from the Risk Adjustment Processing System (RAPS). Regarding the ACA, there is still some question on whether these data validation audits will continue to occur. Meanwhile, congress is driving CMS to expand Risk Adjustment Data Validation (RADV) as quickly as possible.

Scrutiny is going to continue to increase on any risk adjustment revenue programs for health plans, and
plans can’t let their guard down. While this conflict plays out, and however it is resolved, plans are faced with a current reality: Medicare Advantage is a success story, enrollment is going up, and scrutiny on the issue of coding inaccuracy and overpayment is going to intensify. There is no question that while the collection of accurate data is crucial and its level of importance is understood and appreciated by all stakeholders - plans, vendors and the government - the current climate has added even more pressure.

Confidence and Trust that Benefits All

In this environment, health plans need full transparency in order to understand what actions vendors are taking on their behalf. Health plans need assurance that they have access to, and oversight of, coding practices and the ability to approve or deny submission of certain codes, and that the process is managed to ensure accuracy and efficiency.

Likewise, vendors need a reasonable sense of comfort with the information they receive from the physicians and hospital systems that comprise health plan networks. Vendors must work within a “trust but verify” situation and it is in the best interest of both parties, therefore, for vendors to not only have proven internal systems, practices and processes in place, but confidence in the data they’re given. Trust that goes both ways is required to make over-coding, or under payments rare, if not eliminated completely.

Security in the Face of Change

No product or service in this space is guaranteed to be 100% error free, but DST has instituted practices that provide plans with the assurance they need in a period of dramatic change. Again, transparency is of utmost importance throughout the entire process of healthcare delivery and compensation.

DST works collaboratively with plans before beginning any coding activity so that guidelines, and the appropriate level of intelligence and analytics needed to identify potential risks in plans’ data, are understood. That includes looking at provider coding patterns and the performance of physicians in networks. This practice can help identify, or create some leading indicator type information, that might establish expectations or highlight some “red flags,” and sharpens focus on quality review efforts and mitigates risk.

Effective management of coding practices include:

double blind coding

AHIMA and AAPC certification required for all coders

coders with prior experience in risk adjustment coding and documentation

a robust internal training program that ensures that the team fully understands DST’s coding guidelines, approach and methodology

precise identification of the appropriate documentation to support coding

The process is monitored and evaluated throughout by both DST and plans, enabling them to access information and view charts and campaigns, with the authority to audit and review information.

DST understands that your risk adjustment process should not depend solely on finding and adding unrecorded member conditions and diagnoses, but balancing the risk mitigation. It means focusing on overall coding accuracy as the end goal.